Classification of news-related tweets

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dc.contributor.authorDemirsoz, Orhan
dc.contributor.authorOzcan, Rifat
dc.date.accessioned2025-10-24T18:09:33Z
dc.date.available2025-10-24T18:09:33Z
dc.date.issued2017
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractIt is important to obtain public opinion about a news article. Microblogs such as Twitter are popular and an important medium for people to share ideas. An important portion of tweets are related to news or events. Our aim is to find tweets about newspaper reports and measure the popularity of these reports on Twitter. However, it is a challenging task to match informal and very short tweets with formal news reports. In this study, we formulate this problem as a supervised classification task. We propose to form a training set using tweets containing a link to the news and the content of the same news article. We preprocess tweets by removing unnecessary words and symbols and apply stemming by means of morphological analysers. We apply binary classifiers and anomaly detection to this task. We also propose a textual similarity-based approach. We observed that preprocessing of tweets increases accuracy. The textual similarity method obtains results with the highest recognition rate. Success increases in some cases when report text is used with tweets containing a link to the news report within the training set of classification studies. We propose that this study, which is made directly in consideration of tweet texts that measure the trends of national newspaper reports on social media, has a higher significance when compared to Twitter analyses made by using a hashtag. Given the limited number of scientific studies on Turkish tweets, this study makes a contribution to the literature.
dc.identifier.doi10.1177/0165551516653082
dc.identifier.endpage524
dc.identifier.issn0165-5515
dc.identifier.issn1741-6485
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85021877517
dc.identifier.scopusqualityQ1
dc.identifier.startpage509
dc.identifier.urihttps://doi.org/10.1177/0165551516653082
dc.identifier.urihttps://hdl.handle.net/20.500.12899/3679
dc.identifier.volume43
dc.identifier.wosWOS:000404990200006
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSage Publications Ltd
dc.relation.ispartofJournal Of Information Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20251023
dc.subjectPopularity of news; text classification; textual similarity; Turkish tweets; Tweet classification
dc.titleClassification of news-related tweets
dc.typeArticle

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